Imbalanced Domain

Imbalanced domain problems arise when machine learning models are trained on data where classes are not equally represented across different data sources or domains, hindering accurate generalization. Current research focuses on adapting models to handle these imbalances, employing techniques like class-centroid alignment, test-time style shifting, and contrastive learning to mitigate the negative impact of skewed class distributions and domain shifts. These advancements are crucial for improving the robustness and reliability of machine learning in diverse real-world applications, such as medical image analysis, legal text processing, and industrial fault detection, where data scarcity and imbalance are common challenges. The ultimate goal is to develop algorithms that can effectively learn from and generalize to data with varying class distributions across domains.